Advanced holographical and physics inspired deep learning approaches for image transmission through multimode optical fiber

Mohammadrahim Kazemzadeh; Liam Collard; Linda Piscopo; Filippo Pisano; Cristian Ciraci; Massimo De Vittorio; Ferruccio Pisanello

doi:10.1117/12.3017089

18 June 2024 Advanced holographical and physics inspired deep learning approaches for image transmission through multimode optical fiber

Mohammadrahim Kazemzadeh, Liam Collard, Linda Piscopo, Filippo Pisano, Cristian Ciraci, Massimo De Vittorio, Ferruccio Pisanello

Author Affiliations +

Proceedings Volume 13011, Data Science for Photonics and Biophotonics; 130110C (2024) https://doi.org/10.1117/12.3017089
Event: SPIE Photonics Europe, 2024, Strasbourg, France

Abstract

Recent strides in data-driven and deep learning methods have empowered image and wavefront reconstruction in such environments. This breakthrough finds promising roles in biomedical applications like image transmission and holography. Yet, the reconstructed image quality relies on deep learning model effectiveness in understanding transmission mechanisms. In our presentation, we propose two enhancements. First, employs a novel deep learning architecture inspired by light physics, showcasing enhanced image reconstruction quality and broad problem generalization. The second one is an optical method which boosts data variance through holographic encoding, enabling multi-channel image transmission and improved data fusion via deep learning.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Mohammadrahim Kazemzadeh, Liam Collard, Linda Piscopo, Filippo Pisano, Cristian Ciraci, Massimo De Vittorio, and Ferruccio Pisanello "Advanced holographical and physics inspired deep learning approaches for image transmission through multimode optical fiber", Proc. SPIE 13011, Data Science for Photonics and Biophotonics, 130110C (18 June 2024); https://doi.org/10.1117/12.3017089

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available